RED-ML: a novel, effective RNA editing detection method based on machine learning

@article{Xiong2017REDMLAN,
  title={RED-ML: a novel, effective RNA editing detection method based on machine learning},
  author={Heng Xiong and Dongbing Liu and Qiye Li and Mengyue Lei and Liqin Xu and Liang Wu and Zongji Wang and Shancheng Ren and Wangsheng Li and Min Xia and Li-Jing Lu and Haorong Lu and Yong Hou and Shida Zhu and Xin Liu and Yinghao Sun and Jian Wang and Huanming Yang and Kui Wu and Xun Xu and Leo J. Lee},
  journal={GigaScience},
  year={2017},
  volume={6},
  pages={1 - 8}
}
Abstract With the advancement of second generation sequencing techniques, our ability to detect and quantify RNA editing on a global scale has been vastly improved. As a result, RNA editing is now being studied under a growing number of biological conditions so that its biochemical mechanisms and functional roles can be further understood. However, a major barrier that prevents RNA editing from being a routine RNA-seq analysis, similar to gene expression and splicing analysis, for example, is… 

Figures from this paper

TAE-ML:A Random Forest Model for Detecting RNA Editing Sites
TLDR
TAE-ML, a random forest model for detecting RNA editing sites from SNVs in a VCF file called from an RNA-Seq experiment, designed to be a simple python script to make it easy to run on various operating systems that support python.
A-to-I RNA Editing in Cancer: From Evaluating the Editing Level to Exploring the Editing Effects
TLDR
A better understanding of bioinformatics strategies for human cancer A-to-I RNA editing identification is provided and recent advances in related areas are discussed, such as the oncogenic and tumor suppressive effects of RNA editing.
Machine Learning Approaches for RNA Editing Prediction
TLDR
These attempts to develop machine learning models for A-to-I editing prediction in human by training on a large number of highly confident RNA editing sites supported by observational RNA-seq data achieve good performance on held-out test evaluations.
Toward Deep Learning Approaches for Learning Structure Motifs and Classifying Biological Sequences From RNA A-to-I Editing Events
TLDR
It is demonstrated that these newly investigated approaches using large-scale RNA sequencing data offer excellent classification accuracy with a well-optimized convolutional neural network and recurrent neural network classifiers that obtained average area under curves of 0.960 and 0.962, respectively.
Single-nucleotide variants in human RNA: RNA editing and beyond.
TLDR
The controversial history of mining RNA-editing events from RNA-Seq data is described and the corresponding development of methodologies to identify, predict, assess the quality of and catalog RNA-Editing events as well as genomic variants are described.
Genome-wide identification and analysis of A-to-I RNA editing events in bovine by transcriptome sequencing
TLDR
The present study extends the list of RNA editing sites in bovine and provides pipelines that may be used to investigate the editome in other organisms, and finds that a positive correlation exists between expression of ADAR family members and tissue-specific RNA editing.
PRESa2i: incremental decision trees for prediction of Adenosine to Inosine RNA editing sites
RNA editing is a very crucial cellular process affecting protein encoding and is sometimes correlated with the cause of fatal diseases, such as cancer. Thus knowledge about RNA editing sites in a RNA
Investigating RNA editing in deep transcriptome datasets with REDItools and REDIportal
TLDR
This protocol describes bioinformatics procedures to detect RNA editing in RNA-sequencing datasets using REDItools and REDIportal and shows how to identify dysregulated editing at specific recoding sites in post-mortem brain samples of Huntington disease donors.
CREDO: Highly confident disease-relevant A-to-I RNA-editing discovery in breast cancer
TLDR
Application of CREDO to breast cancer data from the Cancer Genome Atlas (TCGA) project discovered highly confident RNA editing with clinical relevance to cancer progression in terms of patient survival.
...
1
2
3
...

References

SHOWING 1-10 OF 37 REFERENCES
Comprehensive analysis of RNA-Seq data reveals extensive RNA editing in a human transcriptome
TLDR
A comprehensive profile of the RNA editome of a male Han Chinese individual is described based on analysis of ∼767 million sequencing reads from poly(A)+, poly( A)− and small RNA samples and 44 editing sites in microRNAs are found, suggesting a potential link between RNA editing and miRNA-mediated regulation.
RES-Scanner: a software package for genome-wide identification of RNA-editing sites
TLDR
RES-Scanner, as a software package written in the Perl programming language, provides a comprehensive solution that addresses read mapping, homozygous genotype calling, de novo RNA-editing site identification and annotation for any species with matching RNA-seq and DNA-seq data.
Analysis and design of RNA sequencing experiments for identifying RNA editing and other single-nucleotide variants.
TLDR
The critical data analysis and experimental design issues of such studies to enable improved systematic investigation of the largely unexplored frontier of single-nucleotide variants in RNA are discussed.
A-to-I RNA editing occurs at over a hundred million genomic sites, located in a majority of human genes.
TLDR
It is found that virtually all adenosines within Alu repeats that form double-stranded RNA undergo A-to-I editing, although most sites exhibit editing at only low levels, doubling the number of edited sites in the human genome.
RNA editing in the human ENCODE RNA-seq data.
TLDR
This study analyzed the long, polyA-selected, unstranded, deeply sequenced RNA-seq data from the ENCODE Project across 14 human cell lines for candidate RNA editing events to find a stronger association of editing and specific genes suggests that the editing of the transcript is more important than the edit of any individual site.
Deciphering the functions and regulation of brain-enriched A-to-I RNA editing
TLDR
It is anticipated that recent technological advancements will aid researchers in acquiring a much deeper understanding of the functions and regulation of RNA editing, and it is imperative to construct a spatiotemporal atlas at the species, tissue and cell levels.
Functional Impact of RNA editing and ADARs on regulation of gene expression: perspectives from deep sequencing studies
TLDR
The growing body of evidence that links RNA editing to other mechanisms of post-transcriptional RNA processing and gene expression regulation including alternative splicing, transcript stability and localization, and the biogenesis and function of microRNAs (miRNAs) is reviewed.
A-to-I RNA Editing: Current Knowledge Sources and Computational Approaches with Special Emphasis on Non-Coding RNA Molecules
TLDR
This work focuses on the current knowledge of RNA editing on ncRNA molecules and provides a few examples of computational approaches to elucidate its biological function.
Genome Sequence-Independent Identification of RNA Editing Sites
TLDR
The GIREMI tool is developed to predict adenosine-to-inosine editing accurately and sensitively from a single RNA-seq data set of modest sequencing depth, and tissue-specific and evolutionary patterns in editing sites in the human population are observed.
...
1
2
3
4
...